CN110785644A - System for determining cause of abnormality of device having rotating member - Google Patents
System for determining cause of abnormality of device having rotating member Download PDFInfo
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- CN110785644A CN110785644A CN201880043504.8A CN201880043504A CN110785644A CN 110785644 A CN110785644 A CN 110785644A CN 201880043504 A CN201880043504 A CN 201880043504A CN 110785644 A CN110785644 A CN 110785644A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
- G01H1/003—Measuring characteristics of vibrations in solids by using direct conduction to the detector of rotating machines
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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- G—PHYSICS
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- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H3/00—Measuring characteristics of vibrations by using a detector in a fluid
- G01H3/04—Frequency
- G01H3/08—Analysing frequencies present in complex vibrations, e.g. comparing harmonics present
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
An abnormality cause determination system (10) for a device having a rotating member is characterized by comprising: acceleration sensors (22 a, 22 b), a pickup sensor (24), and a temperature sensor (26) for observing the state of the rotating member (R) and acquiring measurement data; a measurement data conversion unit (30) for converting the measurement data into two or more different new forms of conversion data; and an abnormality cause determination unit (40) for determining the cause of abnormality of the device by analyzing the conversion data created by the measurement data conversion unit (30).
Description
Technical Field
The present invention relates to an abnormality cause determination system for an apparatus having a rotating member.
Background
Conventionally, there is known a system in which a sensor is attached to a device having a rotating member (for example, a gas turbine, a compressor, a robot including an articulated arm, or the like), and an abnormality of the device is specified based on measurement data obtained from the sensor. However, in order to improve the apparatus having the rotating member, it is desirable to specify not only the abnormal portion but also the cause of the abnormality. As an abnormality cause determination system for a device having a rotary member that can satisfy such a requirement, for example, an abnormality diagnosis system for a rotary machine is disclosed in patent document 1.
The abnormality diagnosis system of patent document 1 includes: a vibration detection sensor provided in a rotary machine as a diagnostic target; an arithmetic processor for converting a detection signal from the vibration detection sensor into vibration data; and an information processing device that performs diagnosis based on the vibration data from the arithmetic processor.
Prior art documents:
patent documents:
patent document 1: japanese patent No. 3834228.
Disclosure of Invention
The problems to be solved by the invention are as follows:
however, although not described in detail in patent document 1, the abnormality diagnosis system for a rotary machine of patent document 1 converts 1 type of frequency-related data generated when the rotary machine is operated, generates one converted data, and specifies the cause of an abnormality based on the converted data. However, in such a case, the cause of the abnormality cannot be specified with high accuracy. And the types of abnormality causes that can be specified are also small.
Accordingly, an object of the present invention is to provide an abnormality cause specifying system for a device having a rotating member, which can specify many kinds of abnormality causes with high accuracy.
Means for solving the problems:
in order to solve the foregoing problems, an abnormality cause determination system of an apparatus having a rotating member according to the present invention is characterized by being an abnormality cause determination system of an apparatus having a rotating member that determines a cause of an abnormality of the apparatus based on measurement data measured while the apparatus having a rotating member is operating; the disclosed device is provided with: a sensor for observing the state of the rotating member and acquiring the measurement data; a measurement data conversion unit that converts the measurement data into two or more different new forms of conversion data; and an abnormality cause specifying unit that specifies a cause of an abnormality of the device by analyzing the conversion data created by the measurement data conversion unit.
According to this configuration, the measurement data conversion unit converts the measurement data into two or more different new forms of conversion data, and the abnormality cause identification unit analyzes the conversion data to identify the cause of abnormality in the apparatus. Thus, the cause of the abnormality can be identified with high accuracy, as compared with the case where, for example, 1 type of frequency-related data generated during the operation of the rotary machine is converted to create one converted data and the cause of the abnormality is identified based on the generated converted data as in the related art. And the number of types of abnormality causes that can be specified can be increased. That is, the abnormality cause determination system of the apparatus having the rotary member according to the present invention can determine a large number of kinds of abnormality causes with high accuracy.
The abnormality cause specification unit may specify the cause of the abnormality of the apparatus by analyzing at least two pieces of conversion data of the two or more different new forms of conversion data created by the measurement data conversion unit in combination.
With this configuration, the effects of the present invention described above can be achieved significantly.
The conversion data may include at least two of the following data: frequency analysis data created in the form of data having frequencies by showing the amplitude of each frequency at a specific time with an orthogonal coordinate system; waterfall data (Waterfall data) made in the form of data showing the amplitude of each frequency by arranging in a specific time range so as to have time in addition to the data of the frequency; bode data (Bode data) created in the form of data having amplitude data and phase by showing the amplitude and phase for each rotational speed in an orthogonal coordinate system; polar coordinate (Polar) data created in the form of data having a phase by showing an amplitude and a phase at each time with a Polar coordinate system; trajectory (Orbit) data created in the form of vibration data in two directions by showing vibration trajectories in a specific time range by making axial center positions determined by vibration data from two directions measured at the same time continuously arrayed; axial center trajectory data created in the form of data having an axial center position in the sliding bearing by showing, in a polar coordinate system, a trajectory per time or a trajectory per rotational speed of the axial center position, which is the vibration center of each of the vibration data from the two directions measured in a specific time range at the same time; cascade (Cascade) data created in the form of data showing the amplitude of each frequency by arranging in a specific rotation speed range so as to have rotation speed in addition to the data of the frequency; and Campbell (Campbell) data made in the form of data having a rotation speed in addition to the data of the frequency by arranging the amplitude of each frequency in a specific rotation speed range and showing in a form different from the cascade data.
According to this configuration, the abnormality cause specifying unit can specify the cause of the abnormality of the apparatus by analyzing the data, the waterfall data, and the other data using, for example, the frequency analysis data, and analyzing the data based on not only the data of the frequency within the specific time range but also the other data. This can bring about a remarkable effect of the present invention.
The measurement data conversion unit may convert the measurement data into the frequency analysis data, the waterfall data, the bode data, the cascade data, or the campbell data without using a characteristic frequency of each data.
According to this structure, the abnormality cause determination system of the device having the rotating member according to the present invention can be used in common regardless of the frequency of each device.
The measurement data conversion unit may generate two or more dimensionless data using two or more different characteristic frequencies of each data when converting the measurement data into the frequency analysis data, the waterfall data, the cascade data, or the campbell data.
According to this configuration, two or more dimensionless data are created from one conversion data, and the two or more dimensionless data can be combined and analyzed. This makes it possible to highlight the feature due to the abnormality and analyze the feature, thereby enabling the cause of the abnormality to be identified with higher accuracy.
The measurement data conversion unit may mark the dimensionless data with a type of characteristic frequency used when the dimensionless processing is performed.
According to this configuration, even when two or more dimensionless data are created from one conversion data, the two or more dimensionless data can be easily managed. This can improve the processing speed when specifying the cause of an abnormality, for example.
For example, the abnormality cause specification unit may analyze the converted data generated by the measurement data conversion unit by comparing the converted data with a judgment model generated in advance.
The measurement data may include first measurement data converted into the two or more conversion data by the measurement data conversion unit and second measurement data not converted by the measurement data conversion unit; the abnormality cause determination unit determines the cause of an abnormality of the device by adding at least one of the second measurement data and control instruction data for the device to at least one conversion data and analyzing the result.
With this configuration, it is possible to specify the cause of an abnormality that cannot be specified by analyzing only the two or more pieces of conversion data. That is, according to this configuration, the types of abnormality causes that can be specified can be further increased, and the accuracy of the specification can be improved.
The measurement data may include first measurement data converted into the two or more conversion data by the measurement data conversion unit and second measurement data not converted by the measurement data conversion unit; the abnormality cause determination unit further determines a cause of an abnormality of the device by analyzing at least one of the second measurement data and control instruction data for the device.
With this configuration, it is possible to specify the cause of an abnormality that cannot be specified by analyzing only the two or more pieces of conversion data. That is, according to this configuration, the types of abnormality causes that can be specified can be further increased, and the accuracy of the specification can be improved.
The invention has the following effects:
the invention provides an abnormality cause specifying system for a device having a rotating member, which can specify a large number of types of abnormality causes with high accuracy.
Drawings
Fig. 1 is a block diagram showing an overall configuration of an abnormality cause determination system of an apparatus having a rotating member according to an embodiment of the present invention;
fig. 2 is a diagram showing an example of the bode data created by the measurement data conversion unit included in the abnormality cause specification system shown in fig. 1;
fig. 3 is a view showing an example of polar coordinate data created by the measurement data conversion unit provided in the abnormality cause specification system;
fig. 4 is a diagram showing an example of trajectory data created by the measurement data conversion unit provided in the abnormality cause specification system;
fig. 5 is a diagram showing an example of frequency analysis data created by the measurement data conversion unit included in the abnormality cause specification system;
fig. 6 is a diagram showing an example of waterfall data created by the measurement data conversion unit included in the abnormality cause determination system;
fig. 7 is a diagram showing an example of cascade data created by the measurement data conversion unit included in the abnormality cause specification system;
fig. 8 is a diagram showing an example of campbell data created by the measurement data conversion unit included in the abnormality cause specification system;
fig. 9 is a diagram showing an example of axial center trajectory data created by the measurement data conversion unit included in the abnormality cause specification system;
fig. 10 is a diagram showing an example of a previously created determination model provided in an abnormality cause specification system for a device having a rotating member according to an embodiment of the present invention.
Detailed Description
(Overall Structure)
Hereinafter, an abnormality cause specifying system of an apparatus having a rotating member according to an embodiment of the present invention will be described with reference to the drawings. Fig. 1 is a block diagram showing an overall configuration of an abnormality cause determination system of an apparatus having a rotating member according to an embodiment of the present invention.
An abnormality cause determination system 10 of a device having a rotating member according to an embodiment of the present invention (hereinafter, simply referred to as "abnormality cause determination system 10") is used to determine a cause of an abnormality of the device having the rotating member R based on measurement data measured while the device is operating (for example, a gas turbine, a steam turbine, a compressor, a hydraulic pump/motor, a robot including an articulated arm driven by an electric motor and a rotating machine, or the like).
Referring to fig. 1, an abnormality cause specifying system 10 includes: two acceleration sensors 22a, 22b and a pick-up (pick-up) sensor 24 provided on the apparatus having the rotating member R; a temperature sensor 26 provided in the vicinity of the device having the rotary member R; a measurement data conversion unit 30 electrically connected to the two acceleration sensors 22a and 22b and the pickup sensor 24, respectively; and an abnormality cause determination unit 40 electrically connected to the measurement data conversion unit 30 and the temperature sensor 26, respectively.
( acceleration sensors 22a, 22b and pickup sensor 24)
The two acceleration sensors 22a and 22b are used to measure vibration data (first measurement data) of the rotary member R generated when the apparatus is operated. The acceleration sensor 22a measures vibration data in a first direction (X-axis direction) orthogonal to the axial direction of the rotary member R, and the acceleration sensor 22b measures vibration data in a second direction (Y-axis direction) orthogonal to the axial direction of the rotary member R and the first direction. The two acceleration sensors 22a and 22b transmit the measured vibration data to the measurement data conversion unit 30. Also, the pickup sensor 24 is used to measure the rotation speed (first measurement data) of the rotating member R. The pickup sensor 24 transmits the measured rotation speed to the measurement data conversion section 30. In addition, instead of using the two acceleration sensors 22a and 22b, for example, a velocity sensor or a displacement sensor may be used to measure the vibration data.
(temperature sensor 26)
The temperature sensor 26 is used to measure temperature data (second measurement data). The temperature sensor 26 transmits the measured temperature data to the abnormality cause determination unit 40.
(measurement data converting part 30)
The measurement data conversion unit 30 converts the vibration data in two directions acquired by the acceleration sensors 22a and 22b and the rotation speed acquired by the pickup sensor 24 into two or more different new forms of conversion data. The measurement data conversion unit 30 is, for example, a computer, and has a memory such as a ROM and a RAM and a CPU, and a program stored in the ROM is executed by the CPU.
Fig. 2 to 8 are diagrams each showing an example of the above-described new form of conversion data generated by the measurement data conversion unit 30. The measurement data conversion unit 30 transmits the created conversion data to the abnormality cause determination unit 40. At this time, the conversion data may be transmitted as numerical data, or may be transmitted as chart data or image data.
Fig. 2 shows an example of bode data created in the form of amplitude data and phase data by representing the amplitude and phase for each rotational speed by an orthogonal coordinate system. As shown in fig. 2, the bode data is expressed as a combination of, for example, a phase map in which the horizontal axis shows the rotational speed and the vertical axis shows the phase, and a map in which the horizontal axis shows the rotational speed and the vertical axis shows the amplitude. Further, the bode data may be created, for example, by extracting the vibration frequency components of 2 or more times and minus 1 times of the rotation synchronization component X (nX), and by extracting the vibration frequency components of 1/n (n is an integer of 2 or more) times ((1/n) X). Note that zX may be formed by multiplying the number of blades z when the blades are provided on the rotary member R and the number of teeth z when the gears are provided by the rotation synchronization component X, and then the following may be created: (nzX) of an integral multiple of 1 or more with respect to zX, ((1/n) zX) of an oscillation frequency component 1/n (n is an integer of 2 or more) times, addition (zX + nX) of a rotation synchronization component X (n is an integer of 1 or more), subtraction (zX-nX) of the rotation synchronization component X, and the like. Hereinafter, the unbalanced vibration component is synonymous with the rotation synchronization component. The risk speed is clear from the bode data.
Fig. 3 shows an example of polar coordinate data created in the form of data having phases by representing amplitude and phase at each time by a polar coordinate system. As shown in fig. 3, the polar coordinate data is a graph in which, for example, scale lines expressed as concentric circles show the amplitude and radial scale lines show the phase. The polar coordinate data shows a trajectory of a vibration vector showing a phase and an amplitude of the unbalanced vibration component (1X), thereby easily showing a characteristic in the event of an abnormality in association with the unbalanced vibration. Especially when the phase changes.
Fig. 4 shows an example of trajectory data created in the form of vibration data in two directions, in which vibration trajectories are represented such that axial center positions determined by vibration data from two directions measured at the same time are continuously aligned within a specific time range. As shown in fig. 4, the trajectory data is represented as a graph in which instantaneous values of the axial position are continuously arranged in an orthogonal coordinate system in which the horizontal axis and the vertical axis both indicate the amplitude, for example. The trajectory data is characterized by a large change in circular shape (or elliptical shape) when a vector force different from imbalance (centrifugal force) such as a reaction force due to contact is generated, and thus, for example, a nonlinear phenomenon such as contact is likely to occur.
Fig. 5 shows an example of frequency analysis data created in the form of data having frequencies by representing the amplitude of each frequency at a specific time in an orthogonal coordinate system. As shown in fig. 5, the frequency analysis data is represented by, for example, two frequency-dependent graphs (a graph in which the horizontal axis shows frequency and the vertical axis shows phase and a graph in which the horizontal axis shows frequency and the vertical axis shows amplitude) obtained by performing fast fourier transform on the vibration data. The characteristics of the frequency within a certain time range can be grasped from the frequency analysis data. Since the abnormal imbalance component is likely to change at a frequency such as a dangerous velocity, it may be made dimensionless at its characteristic frequency.
Fig. 6 shows an example of waterfall data created in the form of data representing the amplitude of each frequency in a row within a specific time range, thereby having time in addition to the data of the frequency. As shown in fig. 6, the waterfall data is expressed, for example, as a color shade map in which the horizontal axis shows frequency, the vertical axis shows time, and colors (or shades) show amplitude. Since data representing time in addition to data of frequency is used with one conversion data, it is liable to exhibit its temporal change in the event of an abnormality. Here, in the case where an abnormality occurs in the device itself, the characteristic thereof appears periodically or gradually becoming larger. On the other hand, when an abnormality occurs due to noise, external disturbance, or the like, its characteristic appears temporarily, and also appears when the apparatus is stopped or is operating at a low speed. Therefore, the waterfall data having the above-described function can grasp the signs thereof with time change when an abnormality occurs due to noise, external disturbance, or the like. Further, by performing dimensionless rotation at the rotation speed, it is easy to exhibit characteristics in the case where an abnormality or a nonlinear phenomenon occurs due to meshing of gears or bearings. Further, by performing dimensionless processing with the natural vibration number, the characteristic is easily exhibited in the case where unstable vibration occurs.
Fig. 7 shows an example of cascade data created in the form of data representing the amplitude of each frequency in a row in a specific rotation speed range and having rotation speed in addition to the data of the frequency. As shown in fig. 7, the cascade data is represented as, for example, a color shade map in which the horizontal axis shows frequency, the vertical axis shows rotational speed, and color (or shade) shows amplitude. The cascade data enables to grasp the state change of each rotation speed. The intrinsic frequency and the dangerous speed are clarified by cascade data.
Fig. 8 shows an example of campbell data created in the form of arranging the amplitude of each frequency in a specific rotation speed range and representing it in a form different from the cascade data so as to have the data of the rotation speed in addition to the data of the frequency. As shown in fig. 8, the campbell data is represented as a graph in which the horizontal axis shows the rotation speed, the vertical axis shows the frequency, and the absolute value of the amplitude is shown in a circle, for example. In addition, the campbell data represents the same content as the concatenated data in a different form. Therefore, the purpose and the like thereof are the same as those of the cascade data, and the description thereof will not be repeated here.
Fig. 9 shows an example of axial center trajectory data created in a form in which the axial center position data, which is the vibration center of the vibration data from two directions measured in a specific time range at the same time, is represented by a polar coordinate system, and the axial center trajectory data is created so as to have the axial center position data in the sliding bearing. As shown in fig. 9, the axial center trajectory data is represented, for example, as a graph in which the horizontal axis shows the axial center position in the horizontal direction in the sliding bearing and the vertical axis shows the axial center position in the vertical direction in the sliding bearing. The axial trajectory data is likely to exhibit its characteristics in the case where an abnormality occurs in the sliding bearing. Here, a trajectory in a straight direction in a Tilting pad (Tilting pad) bearing is drawn, and a floating path of a shaft in a sliding bearing is determined according to a rotation speed. So that it can be determined whether an abnormality occurs by comparison with the path.
The measurement data conversion unit 30 converts the vibration data and the rotation speed measured during the operation of the apparatus into at least two of bode data shown in fig. 2, polar coordinate data shown in fig. 3, trajectory data shown in fig. 4, frequency analysis data shown in fig. 5, waterfall data shown in fig. 6, axial trajectory data shown in fig. 9, cascade data shown in fig. 7, and campbell data shown in fig. 8.
In addition, when no sliding bearing is present in the device, conversion to the axial center trajectory data may not be performed. In this way, the processing speed of the measurement data conversion unit 30 can be increased by not performing unnecessary conversion.
The measurement data conversion unit 30 may be configured to convert the frequency analysis data, the waterfall data, the bode data, the cascade data, or the campbell data into dimensionless data using the characteristic frequency of each data. When the dimensionless frequency analysis data, the waterfall data, the bode data, the cascade data, or the campbell data is performed, one dimensionless data may be created using 1 kind of characteristic frequency, or two or more dimensionless data may be created using two or more different characteristic frequencies.
Here, the characteristic frequency used when dimensionless processing is performed on the frequency analysis data, the waterfall data, the cascade data, and the campbell data includes the natural frequency and the risk rate. When a gear is provided in the rotary member R, the characteristic frequency in the dimensionless case may include the meshing frequency of the gear. When the rotary member R is supported by a rolling bearing, the characteristic frequency in the dimensionless case may include at least one of a bearing inner race defect route (Path), a bearing outer race defect route, a bearing rolling body defect route, and a bearing retainer defect route. When the blades are provided on the rotary member R, the characteristic frequency in the dimensionless case may include a frequency at which the blades pass. On the other hand, the characteristic frequency used when dimensionless processing of the bode data is performed is a risk rate.
In addition, when no gear is present in the device, no dimension is made by the meshing frequency of the gear. Similarly, when the rolling bearing and the sliding bearing are not present in the device, the defect path of the bearing inner ring, the defect path of the bearing outer ring, the defect path of the bearing rolling body, and the defect path of the bearing retainer are not used, and when the blade is not present in the device, the defect path is not used. In this way, the processing speed of the measurement data conversion unit 30 can be increased by not performing unnecessary dimensionless processing.
In the case of the dimensionless processing as described above, the measurement data conversion unit 30 may mark the dimensionless data with the type of the characteristic frequency used in the dimensionless processing.
(abnormality cause determination section 40)
The abnormality cause specification unit 40 analyzes the converted data created by the measurement data conversion unit 30 to specify the cause of the abnormality of the device. The abnormality cause determination unit 40 has a memory such as a ROM or a RAM and a CPU, as in the measurement data conversion unit 30, and a program stored in the ROM is executed by the CPU. The abnormality cause determination unit 40 may be configured as a computer system (i.e., "Artificial Intelligence (AI)") having a self-learning function for artificially realizing an intelligence function such as inference, seed determination, and the like.
The abnormality cause specification unit 40 may analyze at least two pieces of conversion data of the two or more pieces of conversion data created by the measurement data conversion unit 30 as shown in fig. 2 to 9 in combination.
The abnormality cause specification unit 40 may analyze the converted data created by the measurement data conversion unit 30, for example, as shown in fig. 2 to 9, by comparing the converted data with a previously created determination model. Therefore, the abnormality cause specification unit 40 may include a storage device provided separately from the memory in order to store a previously created determination model.
Fig. 10 is a diagram showing an example of a previously created determination model provided in an abnormality cause specification system for a device having a rotating member according to an embodiment of the present invention. In fig. 10, the leftmost column lists the causes of the abnormality and shows the combination candidates of the conversion data used for determining whether each of the causes of the abnormality has occurred. Fig. 10 lists typical 12 abnormality causes, but the abnormality causes are not limited to these.
In fig. 10, "◎" is given to conversion data whose characteristics are remarkably expressed when each of the causes of abnormality is generated, and "○" is given to conversion data whose characteristics are expressed less than those of "◎" when each of the causes of abnormality is generated, and "◎" is given to conversion data whose characteristics are expressed less than those of "◎" when each of the causes of abnormality is generated, and it is easy to analyze if any.
The causes of the abnormality shown in fig. 10 will be described. Fig. 10 also shows combination candidates for analysis for determining that the device is in a normal state. When the apparatus is in a normal state, as shown in fig. 10, at least one of the bode data and the polar coordinate data can be analyzed and specified. The abnormality cause determination unit 40 analyzes these converted data, and determines that the apparatus is in a normal state when the amplitude of the vibration data is smaller than a predetermined value and the phase is reversed when the rotation speed approaches a dangerous speed. Further, the accuracy can be improved by analyzing the waterfall data as well. Further, parsing may be facilitated by parsing the cascade data or the campbell data together.
When an abnormality occurs due to the occurrence of a defect or the adhesion of a substance to the rotating member R ("defect/adhesion on rotating member" in fig. 10), it can be limited by analyzing the polar coordinate data, as shown in fig. 10, because when an abnormality occurs due to the above-mentioned reasons, the magnitude and position of the unbalance change, and the phase changes in addition to the amplitude of the vibration of the unbalanced component (1X) of the rotational synchronization, and the change in the phase is easily expressed in the polar coordinate data (◎), and further, the phase and the amplitude change abruptly when a defect occurs in the rotating member R, and change gradually over a long period of time when a substance adheres.
When an abnormality occurs due to thermal unbalance of the rotating member R, as shown in fig. 10, the abnormality can be limited by analyzing the polar coordinate data because the thermal unbalance is generated by friction, the unbalance due to thermal strain changes with time, particularly, the phase changes in a form of a continuous circle, and the change in the phase is easily reflected on the polar coordinate data (◎).
In the case where an abnormality occurs due to a crack in the rotating shaft, as shown in fig. 10, it can be determined by analyzing cascade data or campbell data because a difference occurs in rigidity between the crack opening direction and the crack closing direction, thereby generating vibration twice (2X) the rotation synchronization component, and the crack may be generated at the manufacturing and assembling stage, and the crack may be likely to be generated at the time of starting, and therefore, the characteristics thereof are easily expressed in cascade data or campbell data (◎) including rotation speed information in addition to the frequency.
When an abnormality occurs due to Friction of the rotating member R, as shown in fig. 10, the abnormality can be limited by analyzing the trajectory data because, when an abnormality occurs due to the above-described reasons, an intermittent external force due to contact acts on the rotor, the shape of the whirling of the unbalanced component (1X) changes greatly in a disturbed manner, and the change in the shape of the whirling vibration is easily expressed in the trajectory data (◎). furthermore, the abnormality can be determined by analyzing at least one of the polar coordinate data and the waterfall data together, for example, when the Friction oscillation (frication whip) is confirmed from the waterfall data, the characteristics are expressed in the vibration frequency component (-1X) that is minus 1 time the rotation synchronization component X.
In the case where the coupling member is coupled to the core and the sliding bearing is misaligned (mismatching (coupling coupled to the core) "in fig. 10), the position of the shaft center in the bearing moves to a position floating or sinking from the normal position, and therefore the characteristic of the bearing changes, and abnormal vibration occurs, as shown in fig. 10, the abnormality of the position of the shaft center can be defined by analyzing the shaft center trajectory data (◎).
When an abnormality occurs due to an angular difference between the coupling member and the rotating member R (in fig. 10, "coupling angular difference"), the abnormality can be limited by analyzing cascade data or campbell data as shown in fig. 10. This is because the above-described reason is generated from the initial stage of the apparatus operation, and therefore, the characteristics thereof do not change with time, and it can be discriminated by analyzing cascade data or campbell data, whether or not a component (2X) 2 times the imbalance component is generated even when the rotation speed is changed. The waterfall data may also be analyzed to determine the waterfall data. Further, the frequency analysis data may be analyzed together, which may make the analysis easy.
In the case where an abnormality occurs due to looseness caused by unbalance of the bearing stand ("bearing stand looseness" in fig. 10), it is possible to limit the occurrence by analyzing waterfall data as shown in fig. 10, because the above-mentioned reason is that harmonic oscillation occurs due to nonlinearity of the supporting rigidity of the bearing stand, and therefore, the influence of the looseness becomes significant and changes over time as the load and the rotational speed increase, and the change is easily expressed in the waterfall data (◎).
When an abnormality occurs due to Oil whirl (Oil whirl) of a rotating member R supported by a sliding bearing, the vibration of a component (1/2) X which becomes half of rotation synchronization is exhibited immediately before the Oil film oscillation occurs, and the component (1/2) X approaches natural vibration (fn) as the rotation speed increases, so that the vibration can be limited by analyzing at least either cascade data and Campbell data (◎) including both rotation speed and frequency information, as shown in FIG. 10.
When an abnormality occurs due to Oil film oscillation (Oil whip) of a rotary member (R) supported by a sliding bearing, vibration of natural vibration (fn) occurs at a rotational speed corresponding to 2 times or more of the frequency of the natural vibration number (fn) of the lowest order, and therefore, as shown in FIG. 10, the abnormality can be limited by analyzing at least one of cascade data and Campbell data (◎) including both rotational speed and frequency information.
In the case where the device having the rotating member R is a Steam turbine, when an abnormality occurs due to Steam excitation (Steam whirl), the abnormality can be limited by analyzing waterfall data as shown in fig. 10. This is because the turbine wheel generates forward whirling, and self-excited vibration (fn) due to the fluid force of the steam is generated. Further, the determination can be made by analyzing at least one of the trajectory data, cascade data, and campbell data together. Further, it is possible to facilitate analysis by analyzing at least one of the frequency analysis data and the axis locus data together.
When the apparatus having the rotating member R is a compressor, if an abnormality occurs due to Gas oscillation (Gas whirl), the abnormality can be limited by analyzing waterfall data as shown in fig. 10. This is because the axial-flow compressor generates backward whirling due to the self-excited vibration (fn) caused by the fluid force of the working fluid. Further, the determination can be made by analyzing at least one of the trajectory data, cascade data, and campbell data together. Further, it is possible to facilitate analysis by analyzing at least one of the frequency analysis data and the axis locus data together.
When the coupling member and the rotary member R are frictionally engaged, if an abnormality occurs due to Damping (damming) caused by the engagement, the vibration of the natural vibration number (fn) occurs at a rotational speed equal to or higher than the dangerous speed (fc), and therefore, as shown in fig. 10, the vibration can be limited by analyzing cascade data or campbell data (◎) including both rotational speed and frequency information because the above-described cause is self-excited vibration (fn) caused by a frictional force generated between the shaft and the engagement member and occurs when the dangerous speed (fc) is exceeded.
When a gear is abnormal, the envelope processing is performed on the measurement data, and the data is used to analyze the frequency analysis data to define the cause of the abnormality, as shown in fig. 10. Further, the cause of the abnormality can be identified by analyzing at least one of the waterfall data, cascade data, and campbell data together. This is because the gear having an abnormality generally has a meshing frequency as its characteristic frequency, its high-order component, and sideband components thereof gradually changing from normal, and therefore, the cause of the abnormality can be identified by checking the change with time and rotational speed together with frequency analysis data at a certain specific time.
When an abnormality occurs in the rolling bearing, the envelope processing is performed on the measurement data, and the data can be used to define the cause of the abnormality by analyzing the frequency analysis data, as shown in fig. 10. Further, the cause of the abnormality can be identified by analyzing at least one of the waterfall data, cascade data, and campbell data together. This is because, when foreign matter or the like is mixed in the rolling bearing, any of the bearing inner ring defect path, the bearing outer ring defect path, the bearing rolling element defect path, the bearing cage defect path, and the like, which are characteristic frequencies, or any of their high-order components shows a rapid change, and therefore, the cause of the abnormality can be identified by confirming the change with time or the rotation speed together with the frequency analysis data at a certain specific time.
The abnormality cause specifying unit 40 specifies the cause of the abnormality of the device as described above, and may output the result to an output device (not shown) such as a display, or may store the result and confirm the result by an arbitrary device (the same as above) provided remotely.
(Effect)
The abnormality cause specification system 10 according to the present embodiment converts measurement data into two or more different new forms of conversion data (for example, conversion data such as one example shown in fig. 2 to 9) by the measurement data conversion unit 30, and specifies the cause of an abnormality of the apparatus by analyzing the conversion data by the abnormality cause specification unit 40. Thus, the cause of the abnormality can be identified with higher accuracy than in the case where, for example, 1 type of frequency-related data generated during the operation of the rotary machine is converted to create one converted data and the cause of the abnormality is identified based on the converted data. Further, the number of types of abnormality causes that can be specified can be increased. That is, the abnormality cause identification system 10 according to the present embodiment can identify many kinds of abnormality causes with high accuracy.
The abnormality cause specification unit 40 can also analyze at least two pieces of conversion data of two or more different new forms created by the measurement data conversion unit 30 in combination, thereby making it possible to bring about a significant effect of the present embodiment described above.
The abnormality cause determination unit 40 can determine the cause of an abnormality of the apparatus by analyzing at least two of the conversion data shown in fig. 2 to 9, for example, frequency analysis data, waterfall data, and other data, and analyzing the data not only based on the data of the frequency in a specific time range but also based on other data. This can bring about a remarkable effect of the present invention.
The measurement data conversion unit 30 performs dimensionless processing using the characteristic frequency of each data as described above. Thus, the abnormality cause specifying system according to the present embodiment can be used in common without depending on the frequency of each device.
As described above, the measurement data conversion unit 30 creates two or more dimensionless data using two or more different characteristic frequencies of each data. In this way, since two or more dimensionless data are created from one conversion data, the abnormality cause specification unit 40 can analyze the two or more dimensionless data in combination. As a result, the abnormality cause specification system 10 according to the present embodiment can perform analysis after highlighting the features due to the abnormality, and can specify the cause of the abnormality with higher accuracy.
Further, since the measurement data conversion unit 30 marks the dimensionless data, even when two or more dimensionless data are created from one conversion data, the two or more dimensionless data can be easily managed. This can improve the processing speed when specifying the cause of an abnormality, for example.
Further, by adding the monitoring data (second measurement data) of the operating state such as the temperature data, the pressure data, and the output data of the apparatus, which are not converted by the measurement data conversion unit 30, to one or more conversion data (for example, waterfall data having a time axis) and analyzing the data, it is possible to improve the accuracy of specifying the cause of the abnormality and further increase the kinds of the determinable causes of the abnormality.
(modification example)
In the above embodiment, the case where the two acceleration sensors 22a and 22b and the pickup sensor 24 are provided in the apparatus having the rotary member R to measure the vibration data and the rotation speed as the first measurement data has been described, but the present invention is not limited thereto. For example, the sound data may be measured as the first measurement data by disposing a microphone in the vicinity of the rotary member R. Further, it is also possible to measure torque data as the first measurement data by mounting a torque meter on the device having the rotating member R. In addition, in the case where the device having the rotating member R is motor-driven, motor current data may be measured as the first measurement data.
In the above embodiment, the case where the abnormality cause determining unit 30 adds the second measurement data (the monitoring data of the operation state such as the temperature data, the pressure data, and the output data of the device) that is not converted by the measurement data converting unit 30 to one or more converted data and analyzes the data has been described, but the present invention is not limited to this. For example, the abnormality cause specification unit 30 may analyze one or more pieces of conversion data by adding control command data for the apparatus (for example, control command data for moving the articulated arm for a robot) to the one or more pieces of conversion data. Thus, the abnormality cause system 10 can further increase the kinds of determinable causes of the abnormality. That is, the abnormality cause determination unit 30 may add at least one of the second measurement data that is not converted by the measurement data conversion unit 30 and the control command data for the device to one or more conversion data and analyze the data.
The abnormality cause specification unit 30 may specify the cause of the abnormality by analyzing at least two of the converted data converted by the measurement data conversion unit 30 and analyzing at least one of the second measurement data and the control command data for the device. Thus, the cause of the abnormality, which cannot be identified only by analyzing the two or more conversion data, can be identified. That is, according to this configuration, the types of abnormality causes that can be specified can be further increased, and the accuracy of the specification can be improved. In this case, at least one of the second measurement data and the control command data may be associated with the conversion data, for example, by a time axis.
In the above embodiment, the measurement data conversion unit 30 and the abnormality cause determination unit 40 are configured as separate devices, and the abnormality cause determination unit 40 may be configured as so-called Artificial Intelligence (AI), but the present invention is not limited to this case. For example, the measurement data conversion unit 30 and the abnormality cause determination unit 40 may be configured as a single device. And the one device may also be configured as a so-called Artificial Intelligence (AI).
In the above embodiment, the case where the abnormality cause specifying system 10 specifies the cause of an abnormality of an apparatus having one rotating member R has been described, but the present invention is not limited thereto. That is, the abnormality cause specifying system 10 may specify the cause of abnormality of an apparatus having two or more rotating members R.
From the above description, it is clear that many modifications and other embodiments of the present invention will be apparent to those skilled in the art. Accordingly, the foregoing description is to be construed as illustrative only and is for the purpose of teaching those skilled in the art the best mode of carrying out the invention. The details of the structure and/or function of the present invention may be substantially changed without departing from the spirit of the present invention.
Description of the symbols:
10 an abnormality cause determination system;
22a, 22b acceleration sensors;
24 a pickup sensor;
26 a temperature sensor;
30 a measurement data conversion unit;
40 an abnormality cause determination unit;
r rotating member.
Claims (9)
1. An abnormality cause determination system for an apparatus having a rotary member,
an abnormality cause determination system that is a device having a rotating member that determines a cause of an abnormality of the device based on measurement data measured while the device having the rotating member is operating; the disclosed device is provided with:
a sensor for observing the state of the rotating member and acquiring the measurement data;
a measurement data conversion unit that converts the measurement data into two or more different new forms of conversion data; and
an abnormality cause specifying unit configured to specify a cause of an abnormality of the device by analyzing the converted data created by the measurement data converting unit.
2. The abnormality cause determination system of an apparatus having a rotating member according to claim 1,
the abnormality cause specification unit specifies the cause of the abnormality of the device by analyzing at least two pieces of conversion data of the two or more different new forms of conversion data created by the measurement data conversion unit in combination.
3. The abnormality cause determination system of an apparatus having a rotating member according to claim 1 or 2,
the conversion data includes at least two of the following data:
frequency analysis data created in the form of data having frequencies by showing the amplitude of each frequency at a specific time with an orthogonal coordinate system;
waterfall data made in the form of data showing the amplitude of each frequency by arranging in a specific time range so as to have time in addition to the data of the frequency;
bode data created in the form of data having amplitude data and phase by showing the amplitude and phase of each rotational speed with an orthogonal coordinate system;
polar coordinate data created in the form of data having a phase by showing an amplitude and a phase at each time with a polar coordinate system;
trajectory data created in such a manner that the positions of the axes determined by the vibration data from two directions measured at the same time are continuously arranged within a specific time range to show the vibration trajectory, thereby having vibration data from two directions;
axial center trajectory data created in the form of data having an axial center position in the sliding bearing by showing, in a polar coordinate system, a trajectory per time or a trajectory per rotational speed of the axial center position, which is the vibration center of each of the vibration data from the two directions measured in a specific time range at the same time;
cascade data made in the form of data showing the amplitude of each frequency by arranging in a specific rotation speed range so as to have the rotation speed in addition to the data of the frequency; and
campbell data is created in the form of data having rotational speed in addition to data for frequency by arranging the amplitude of each frequency within a specific rotational speed range and showing it in a form different from the cascade data.
4. The abnormality cause determination system of an apparatus having a rotating member according to claim 3,
the measurement data conversion unit converts the measurement data into the frequency analysis data, the waterfall data, the bode data, the cascade data, or the campbell data, and performs dimensionless processing using a characteristic frequency of each data.
5. The abnormality cause determination system of an apparatus having a rotating member according to claim 4,
the measurement data conversion unit generates two or more dimensionless data by using two or more different characteristic frequencies of each data when converting the measurement data into the frequency analysis data, the waterfall data, the cascade data, or the campbell data.
6. The abnormality cause determination system of an apparatus having a rotating member according to claim 5,
the measurement data conversion unit marks the type of the characteristic frequency used for the dimensionless processing on the dimensionless data.
7. The abnormality cause determination system of an apparatus having a rotating member according to any one of claims 1 to 6,
the abnormality cause determination unit analyzes the converted data generated by the measurement data conversion unit by comparing the converted data with a judgment model generated in advance.
8. The abnormality cause determination system of an apparatus having a rotating member according to any one of claims 1 to 7,
the measurement data includes first measurement data converted into two or more of the conversion data by the measurement data conversion portion and second measurement data not converted by the measurement data conversion portion;
the abnormality cause determination unit determines the cause of an abnormality of the device by adding at least one of the second measurement data and control instruction data for the device to at least one conversion data and analyzing the result.
9. The abnormality cause determination system of an apparatus having a rotating member according to any one of claims 1 to 7,
the measurement data includes first measurement data converted into two or more of the conversion data by the measurement data conversion portion and second measurement data not converted by the measurement data conversion portion;
the abnormality cause determination unit further determines a cause of an abnormality of the device by analyzing at least one of the second measurement data and control instruction data for the device.
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| JP2017-127421 | 2017-06-29 | ||
| JP2017127421A JP6944285B2 (en) | 2017-06-29 | 2017-06-29 | Abnormal cause identification system for devices with rotating members |
| PCT/JP2018/019183 WO2019003699A1 (en) | 2017-06-29 | 2018-05-17 | System for specifying cause of abnormality in device having rotating member |
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| CN110785644A true CN110785644A (en) | 2020-02-11 |
| CN110785644B CN110785644B (en) | 2021-08-20 |
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| EP (1) | EP3647761A4 (en) |
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| CN114264366A (en) * | 2021-12-22 | 2022-04-01 | 南水北调东线江苏水源有限责任公司 | Method for monitoring multi-angle vibration components of pump shell of water pump unit |
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| US11143545B2 (en) * | 2019-02-12 | 2021-10-12 | Computational Systems, Inc. | Thinning of scalar vibration data |
| US10705861B1 (en) | 2019-03-28 | 2020-07-07 | Tableau Software, LLC | Providing user interfaces based on data source semantics |
| US11783266B2 (en) * | 2019-09-18 | 2023-10-10 | Tableau Software, LLC | Surfacing visualization mirages |
| US11550815B2 (en) | 2020-07-30 | 2023-01-10 | Tableau Software, LLC | Providing and surfacing metrics for visualizations |
| US11397746B2 (en) | 2020-07-30 | 2022-07-26 | Tableau Software, LLC | Interactive interface for data analysis and report generation |
| JP7338584B2 (en) * | 2020-08-07 | 2023-09-05 | トヨタ自動車株式会社 | Abnormality determination device |
| US11579760B2 (en) | 2020-09-08 | 2023-02-14 | Tableau Software, LLC | Automatic data model generation |
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| JP2019011976A (en) | 2019-01-24 |
| US20210088418A1 (en) | 2021-03-25 |
| CN110785644B (en) | 2021-08-20 |
| WO2019003699A1 (en) | 2019-01-03 |
| JP6944285B2 (en) | 2021-10-06 |
| EP3647761A4 (en) | 2021-03-31 |
| US11125654B2 (en) | 2021-09-21 |
| EP3647761A1 (en) | 2020-05-06 |
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